Typically, as transistor count in a chip increases, operating frequency to match speed requirement also increases. With this, power consumption and dissipation in the chip also increases multiple times. Conventionally, power consumption is an over-riding concern in the design of a processor of computer systems. Dynamic voltage frequency scaling (DVFS) is a power management technique, that dynamically scales voltage and frequency (v-f) settings of the processor so as to provide just-enough speed to process system workload at a given point of time. Further, scaling down of voltage levels results in a quadratic reduction in processors dynamic power consumption. Further, conventional DVFS power management techniques have demonstrated its ability to achieve energy savings while keeping performance degradation under acceptable limits. However, workload prediction of the processor is essentially an important consideration for performing the DVFS. Accurate workload prediction of the processor can lead to accuracy in frequency generation which in turn can lead to accuracy in voltage generation.
However, an important challenge in managing generation of frequency and voltage by the processor is that the processor is required to cater to variable workload and the processor is also required to be capable of estimating or anticipating future workload. In the absence of effective workload prediction, the processor is not capable of allocating appropriate computing resources to meet a workload level increase or, conversely, reduce resource under-utilization in the event of a decreasing workload level.
Currently, workload prediction of processors use fixed model for forecasting future workload irrespective of varying workload history. In this respect, one fixed model may not give accurate forecasting of future workload specifically with the varying processor workload profile. Further, conventional fixed forecastable models used for forecasting future workload may not have good accuracy for different workload scenarios. In an example, for a particular workload profile, the fixed forecastable model may give prediction accuracy but the same model may not provide improved accuracy at a different workload profile. Therefore, inaccurate forecasting may lead to generation of incorrect frequency and voltage, that does not minimize power dissipation.
Significant effort has been channeled towards making these forecasts of future workload increasingly accurate to enable the processor to optimize energy consumption based on the predicted workload. Several factors contribute to the accuracy of forecasting, including use of extensive historical data and a reasonable time interval between successive historical data. However, as the historical data profile changes, the accuracy of predicting different workload scenarios also changes which leads to incorrect frequency and voltage generation.
Hence, there is a need for selecting an appropriate forecasting model to ensure optimized energy consumption by the processor that alleviates problems associated with conventional models.